Technical product management
ISCO-08 2149, 2433Product manager, Offer manager, Product line manager, Product marketing manager, Range manager, Product owner (connected offers)
Headcount need, in FTE
Target AI skills
level 1 to 4Job skills
Growing in value
- Range strategy and portfolio decisions
- Field insight from installers and specifiers
- Business cases for connected and software offers
- Coordinating R&D, plants and sales at launch
Losing value
- Compiling competitor benchmarks
- Writing product sheets and catalogue content
- Producing sales training materials
- Formatting launch documents
How the job will change
A product manager in electrical equipment produces a lot of written and analytical material: competitor benchmarks, product sheets, catalogue content in several languages, launch kits for sales teams, pricing analyses. AI now drafts most of this, compares competing ranges from public documentation and summarises feedback from distributors and installers. The time spent compiling and formatting falls noticeably.
The job moves towards decisions: which references to retire, which connected services to add to a range, how to price software alongside hardware. A good product manager tomorrow will spend more time with installers, specifiers and plants, and will be judged on the quality of their arbitrations rather than the volume of documents they produce.
- 2026-2027
- Benchmarks, product sheets and translations drafted with AI assistants.
- 2028-2030
- Product data and catalogue content generated from a single reference base.
- 2031+
- Product roles focus on portfolio strategy and connected offers.
The shift to connected and software offers needs product managers comfortable with recurring revenue and data. This profile is rare among experienced hardware product managers, and software companies compete for it. See the seniority outlook below.
What if you hired fewer juniors?
Your 2036 seniors are the juniors you hire today.
Advanced settings modified
2026 2036
The AI Cookbook 2026
albert's guide to cut through the noise around generative AI and turn it into workforce decisions.
- What AI actually changes in jobs and skills
- Why the junior pipeline matters more than most companies realise
- How to integrate AI into workforce planning

Run these scenarios on your actual workforce
AI Impact Diagnostic: 6 to 8 weeks, your data in albert, three costed scenarios and the projected seniority mix for each job family.
How the numbers are built
Every headcount figure combines four assumptions. Three come pre-filled from public research and job family defaults. The strategic ceiling is yours to set.
Seniority outlook
A flow model with three levels of experience in the profession. Promotion and exit rates are set so that today's mix stays stable when hiring does not change: any gap you see comes from the junior hiring cut alone.
Sources
- International Labour OrganizationGenerative AI and jobs: a refined global index of occupational exposure (2025)
- ILO datasetTask-level GenAI exposure scores by ISCO-08 occupation
- AnthropicAnthropic Economic Index (June 2026 release, April and May 2026 usage data)
- Stanford Digital Economy LabCanaries in the Coal Mine? Six facts about the recent employment effects of AI